{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-Minute Tutorial: Getting Started\n",
    "\n",
    "Welcome to ``brainpy.state``! This quick tutorial will get you up and running with your first neural simulation in just a few minutes.\n",
    "\n",
    "## What You'll Learn\n",
    "\n",
    "- How to create neurons\n",
    "- How to build simple networks\n",
    "- How to run simulations\n",
    "- How to visualize results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Import Libraries\n",
    "\n",
    "First, let's import the necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:17:12.262478Z",
     "start_time": "2025-10-10T07:17:12.257706Z"
    }
   },
   "outputs": [],
   "source": [
    "import jax\n",
    "\n",
    "import brainpy\n",
    "import brainstate\n",
    "import brainunit as u\n",
    "import braintools\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Create Your First Neuron\n",
    "\n",
    "Let's create a simple Leaky Integrate-and-Fire (LIF) neuron:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:17:12.278071Z",
     "start_time": "2025-10-10T07:17:12.273488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created a LIF neuron!\n"
     ]
    }
   ],
   "source": [
    "# Set simulation time step\n",
    "brainstate.environ.set(dt=0.1 * u.ms)\n",
    "\n",
    "# Create a single LIF neuron\n",
    "neuron = brainpy.state.LIF(\n",
    "    1,\n",
    "    V_rest=-65. * u.mV,      # Resting potential\n",
    "    V_th=-50. * u.mV,        # Spike threshold\n",
    "    V_reset=-65. * u.mV,     # Reset potential\n",
    "    tau=10. * u.ms,          # Membrane time constant\n",
    ")\n",
    "\n",
    "print(\"Created a LIF neuron!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Simulate the Neuron\n",
    "\n",
    "Now let's inject a constant current and see how the neuron responds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:18:40.403662Z",
     "start_time": "2025-10-10T07:18:40.322794Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation complete! Recorded 2000 time steps.\n"
     ]
    }
   ],
   "source": [
    "# Initialize neuron state\n",
    "brainstate.nn.init_all_states(neuron)\n",
    "\n",
    "# Define simulation parameters\n",
    "duration = 200. * u.ms\n",
    "dt = brainstate.environ.get_dt()\n",
    "times = u.math.arange(0. * u.ms, duration, dt)\n",
    "\n",
    "# Input current (constant)\n",
    "I_input = 20.0 * u.mA\n",
    "\n",
    "# Run simulation and record membrane potential\n",
    "def step_run(t):\n",
    "    with brainstate.environ.context(t=t):\n",
    "        neuron(I_input)\n",
    "        return neuron.V.value, neuron.get_spike()\n",
    "\n",
    "voltages, spikes = brainstate.transform.for_loop(step_run, times)\n",
    "\n",
    "print(f\"Simulation complete! Recorded {len(times)} time steps.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Visualize the Results\n",
    "\n",
    "Let's plot the membrane potential over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:18:43.855438Z",
     "start_time": "2025-10-10T07:18:43.755841Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of spikes: 17\n",
      "Average firing rate: 85.00 Hz\n"
     ]
    }
   ],
   "source": [
    "# Convert to appropriate units for plotting\n",
    "times_plot = times.to_decimal(u.ms)\n",
    "voltages_plot = voltages.to_decimal(u.mV)\n",
    "\n",
    "# Create plot\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(times_plot, voltages_plot, linewidth=2)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Membrane Potential (mV)')\n",
    "plt.title('LIF Neuron Response to Constant Input')\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Count spikes\n",
    "n_spikes = int(u.math.sum(spikes != 0))\n",
    "firing_rate = n_spikes / (duration.to_decimal(u.second))\n",
    "print(f\"Number of spikes: {n_spikes}\")\n",
    "print(f\"Average firing rate: {firing_rate:.2f} Hz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Create a Network of Neurons\n",
    "\n",
    "Now let's create a small network with excitatory and inhibitory populations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:19:06.974038Z",
     "start_time": "2025-10-10T07:19:06.958649Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created network with 100 neurons\n"
     ]
    }
   ],
   "source": [
    "class SimpleEINet(brainstate.nn.Module):\n",
    "    def __init__(self, n_exc=80, n_inh=20):\n",
    "        super().__init__()\n",
    "        self.n_exc = n_exc\n",
    "        self.n_inh = n_inh\n",
    "        self.num = n_exc + n_inh\n",
    "        \n",
    "        # Create neurons\n",
    "        self.neurons = brainpy.state.LIF(\n",
    "            self.num,\n",
    "            V_rest=-65. * u.mV,\n",
    "            V_th=-50. * u.mV,\n",
    "            V_reset=-65. * u.mV,\n",
    "            tau=10. * u.ms,\n",
    "            V_initializer=braintools.init.Normal(-65., 5., unit=u.mV)\n",
    "        )\n",
    "        \n",
    "        # Excitatory to all projection\n",
    "        self.E2all = brainpy.state.AlignPostProj(\n",
    "            comm=brainstate.nn.EventFixedProb(n_exc, self.num, 0.1, 0.6*u.mS),\n",
    "            syn=brainpy.state.Expon.desc(self.num, tau=2. * u.ms),\n",
    "            out=brainpy.state.CUBA.desc(),\n",
    "            post=self.neurons,\n",
    "        )\n",
    "        \n",
    "        # Inhibitory to all projection\n",
    "        self.I2all = brainpy.state.AlignPostProj(\n",
    "            comm=brainstate.nn.EventFixedProb(n_inh, self.num, 0.1, -5.0*u.mS),\n",
    "            syn=brainpy.state.Expon.desc(self.num, tau=2. * u.ms),\n",
    "            out=brainpy.state.CUBA.desc(),\n",
    "            post=self.neurons,\n",
    "        )\n",
    "    \n",
    "    def update(self, input_current):\n",
    "        # Get spikes from previous time step\n",
    "        spikes = self.neurons.get_spike()\n",
    "        \n",
    "        # Update projections\n",
    "        self.E2all(spikes[:self.n_exc])  # Excitatory spikes\n",
    "        self.I2all(spikes[self.n_exc:])  # Inhibitory spikes\n",
    "        \n",
    "        # Update neurons\n",
    "        self.neurons(input_current)\n",
    "        \n",
    "        return self.neurons.get_spike()\n",
    "\n",
    "# Create network\n",
    "net = SimpleEINet(n_exc=80, n_inh=20)\n",
    "print(f\"Created network with {net.num} neurons\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Simulate the Network\n",
    "\n",
    "Let's run the network and visualize its activity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:19:11.531447Z",
     "start_time": "2025-10-10T07:19:10.189416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "015f055bef8948e79efe5b082a59fc25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network simulation complete!\n"
     ]
    }
   ],
   "source": [
    "# Initialize network states\n",
    "brainstate.nn.init_all_states(net)\n",
    "\n",
    "# Simulation parameters\n",
    "duration = 500. * u.ms\n",
    "times = u.math.arange(0. * u.ms, duration, dt)\n",
    "I_ext = 16 * u.mA  # External input current\n",
    "\n",
    "# Run simulation\n",
    "spike_history = brainstate.transform.for_loop(\n",
    "    lambda t: net.update(I_ext),\n",
    "    times,\n",
    "    pbar=brainstate.transform.ProgressBar(10)\n",
    ")\n",
    "\n",
    "print(\"Network simulation complete!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Visualize Network Activity (Raster Plot)\n",
    "\n",
    "Create a raster plot showing when each neuron fired:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T07:19:55.083063Z",
     "start_time": "2025-10-10T07:19:49.506767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total spikes: 1586\n",
      "Average firing rate: 31.72 Hz\n"
     ]
    }
   ],
   "source": [
    "import jax\n",
    "spike_history = jax.block_until_ready(spike_history)\n",
    "\n",
    "# Find spike times and neuron indices\n",
    "t_indices, n_indices = u.math.where(spike_history != 0)\n",
    "\n",
    "# Convert to plottable format\n",
    "spike_times = times[t_indices].to_decimal(u.ms)\n",
    "\n",
    "# Create raster plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(spike_times, n_indices, s=1, c='black', alpha=0.5)\n",
    "\n",
    "# Mark excitatory and inhibitory populations\n",
    "plt.axhline(y=net.n_exc, color='red', linestyle='--', alpha=0.5, label='E/I boundary')\n",
    "\n",
    "plt.xlabel('Time (ms)', fontsize=12)\n",
    "plt.ylabel('Neuron Index', fontsize=12)\n",
    "plt.title('Network Activity (Raster Plot)', fontsize=14)\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Add text annotations\n",
    "plt.text(10, net.n_exc/2, 'Excitatory', fontsize=10, bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))\n",
    "plt.text(10, net.n_exc + net.n_inh/2, 'Inhibitory', fontsize=10, bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.5))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Calculate statistics\n",
    "total_spikes = len(t_indices)\n",
    "avg_rate = total_spikes / (net.num * duration.to_decimal(u.second))\n",
    "print(f\"Total spikes: {total_spikes}\")\n",
    "print(f\"Average firing rate: {avg_rate:.2f} Hz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Congratulations! 🎉 You've just:\n",
    "\n",
    "1. ✅ Created individual neurons with physical units\n",
    "2. ✅ Simulated neuron dynamics with input currents\n",
    "3. ✅ Built a network with excitatory and inhibitory populations\n",
    "4. ✅ Connected neurons with synaptic projections\n",
    "5. ✅ Visualized network activity\n",
    "\n",
    "## Next Steps\n",
    "\n",
    "Now that you've completed your first simulation, you can:\n",
    "\n",
    "- **Learn more concepts**: Read the [Core Concepts](../core-concepts/architecture.rst) guide\n",
    "- **Follow tutorials**: Try the [Basic Tutorials](../tutorials/basic/01-lif-neuron.ipynb) for deeper understanding\n",
    "- **Explore examples**: Check out the [Examples Gallery](../examples/gallery.rst) for real-world models\n",
    "- **Experiment**: Modify the network parameters and see what happens!\n",
    "\n",
    "### Try These Experiments\n",
    "\n",
    "1. Change the connection probability in the network\n",
    "2. Adjust the excitatory/inhibitory balance\n",
    "3. Add more neuron populations\n",
    "4. Try different input currents or patterns\n",
    "\n",
    "Happy modeling! 🧠"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Ecosystem-py",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
